Top 50 Companies by Revenue (USD Millions)

Description : This dataset contains information on the largest companies in the world ranked by their revenue in USD millions. It includes key financial metrics and details about each company, making it a valuable resource for analysis and comparison.

This list comprises the world's largest companies by consolidated revenue, according to the Fortune Global 500 2024 rankings and other sources. American retail corporation Walmart has been the world's largest company by revenue since 2014. The list is limited to the largest 50 companies, all of which have annual revenues exceeding US$130 billion. This list is incomplete, as not all companies disclose their information to the media or general public. Out of 50 largest companies 23 are American, 17 Asian and 10 European.

Features :

• Rank: The rank of the company based on its revenue.
• Company_Name: The name of the company.
• Industry: The industry in which the company operates.
• Revenue (USD Millions): The total revenue of the company in millions of USD.
• Profit (USD Millions): The total profit of the company in millions of USD.
• Number of Employees: The total number of employees working for the company.
• Headquarters: The country where the company's headquarters is located.

Source : The data has been sourced from the Wikipedia page on List of Largest Companies by Revenue.

Usage : This dataset can be used for various analyses, including : - Financial performance comparisons across industries. - Visualization of the largest global companies. - Insights into employment statistics in relation to revenue.

Beginner-Friendly : This dataset is suitable for beginners looking to practice data analysis, data visualization, and financial comparisons. It provides a straightforward structure with easily understandable features, making it an excellent starting point for those new to data science.

Overview This dataset contains information about the largest companies in the United States by revenue. It includes key attributes such as company name, industry, annual revenue, profit, number of employees, and the state where the company is headquartered. The dataset provides valuable insights into the financial and operational aspects of these major corporations.

Columns Rank: Ranking of the company based on its annual revenue. Name: Name of the company. Industry: Industry in which the company operates. Revenue: Annual revenue of the company in millions of dollars. Profit: Annual profit of the company in millions of dollars. Employees: Number of employees working for the company. State: State where the company’s headquarters are located. Key Insights Revenue Distribution: Significant variation in revenue among the top companies, with some generating much higher revenues. Profit Margins: Wide variation in profit margins, indicating different levels of profitability across industries. Employee Numbers: Disparity in the number of employees, reflecting differences in business models and operational scales. Geographic Spread: Companies are headquartered in various states, with certain states having a higher concentration of large companies. Potential Uses Industry Analysis: Understand trends and performance in different industries. Economic Research: Analyze the economic impact of these large companies. Business Strategy: Inform business strategies and market analysis. Educational Purposes: Use as a case study for business and economic courses. Future Work In-Depth Industry Analysis: Explore specific industries to identify trends and outliers. Time-Series Analysis: Analyze trends over time if historical data becomes available. Comparative Analysis: Compare with similar datasets from other countries. Advanced Visualization: Create interactive dashboards for better data presentation. This dataset is a valuable resource for anyone interested in the financial and operational characteristics of the largest companies in the United States.

Data Description

The dataset provided includes information about various companies, their stock symbols, financial metrics such as price-to-book ratio and share price, as well as details about their origin countries. Additionally, the dataset contains frequency distribution information for certain ranges of price-to-book ratios and share prices.

About Data

The dataset appears to be a compilation of financial data for different companies, likely for investment analysis or comparison purposes. It includes the following key components:

• Rank: Rank of the company based on some criteria (not explicitly mentioned).
• Company: Name of the company.
• Stock Symbol: Symbol used to identify the company's stock in trading.
• Price to Book Ratio: Financial metric indicating the relationship between a company's market value and its book value.
• Share Price (USD): Price of a single share of the company's stock in US dollars.
• Company Origin: Country where the company is based.
• Label Count: Frequency distribution information for certain ranges of price-to-book ratios and share prices.

This dataset can be utilized for various financial analyses such as company valuation, comparison of financial metrics across companies, and investment decision-making.

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027. What is Big data? Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. Big data analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

United States Gross Value Added (GVA): saar data was reported at 19,931.717 USD bn in Mar 2018. This records an increase from the previous number of 19,699.332 USD bn for Dec 2017. United States Gross Value Added (GVA): saar data is updated quarterly, averaging 5,305.278 USD bn from Mar 1959 (Median) to Mar 2018, with 237 observations. The data reached an all-time high of 19,931.717 USD bn in Mar 2018 and a record low of 517.130 USD bn in Mar 1959. United States Gross Value Added (GVA): saar data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s USA – Table US.AB074: Integrated Macroeconomic Accounts: Total Economy and Sectors: Selected Aggregates.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Industry, PA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 14,944, while the mean income for the highest quintile (20% of households with the highest income) is 153,317. This indicates that the top earners earn 10 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 194,891, which is 127.12% higher compared to the highest quintile, and 1304.14% higher compared to the lowest quintile.

https://i.neilsberg.com/ch/industry-pa-mean-household-income-by-quintiles.jpeg" alt="Mean household income by quintiles in Industry, PA (in 2022 inflation-adjusted dollars))">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Industry median household income. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Industry, Maine, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 20,194, while the mean income for the highest quintile (20% of households with the highest income) is 176,380. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 274,320, which is 155.53% higher compared to the highest quintile, and 1358.42% higher compared to the lowest quintile.

https://i.neilsberg.com/ch/industry-me-mean-household-income-by-quintiles.jpeg" alt="Mean household income by quintiles in Industry, Maine (in 2022 inflation-adjusted dollars))">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Industry town median household income. You can refer the same here

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_9d1105cafc80c03d6273dcdeb5ad415a/view

Corporate Profits in the United States increased to 3259.41 USD Billion in the second quarter of 2025 from 3252.44 USD Billion in the first quarter of 2025. This dataset provides the latest reported value for - United States Corporate Profits - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In 2020, global gross domestic product declined by 6.7 percent as a result of the coronavirus (COVID-19) pandemic outbreak. In Latin America, overall GDP loss amounted to 8.5 percent.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_8ab3c73416b81c8a20ac8a93a1c80a40/view

This statistic shows the ranking of the global top 10 biotech and pharmaceutical companies worldwide, based on revenue. The values are based on a 2025 database. U.S. pharmaceutical company Pfizer was ranked first, with a total revenue of around ** billion U.S. dollars. Biotech and pharmaceutical companiesPharmaceutical companies are best known for manufacturing pharmaceutical drugs. These drugs have the aim to diagnose, to cure, to treat, or to prevent diseases. The pharmaceutical sector represents a huge industry, with the global pharmaceutical market being worth around *** trillion U.S. dollars. The best known top global pharmaceutical players are Pfizer, Merck, and Johnson & Johnson from the U.S., Novartis and Roche from Switzerland, Sanofi from France, etc. Most of these companies are involved not only in pure pharmaceutical business, but also manufacture medical technology and consumer health products, vaccines, etc. There are both pure play biotechnology companies and pharmaceutical companies which among other products also produce biotech products within their biotechnological divisions. Most of the leading global pharmaceutical companies have biopharmaceutical divisions. Although not a pure play biotech firm, Roche from Switzerland is among the companies with the largest revenues from biotechnology products worldwide. In contrast, California-based company Amgen was one of the world’s first large pure play biotech companies. Biotech companies use biotechnology to generate their products, most often medical drugs or agricultural genetic engineering. The latter segment is dominated by companies like Bayer CropScience and Syngenta. The United Nations Convention on Biological Diversity defines biotechnology as follows: "Any technological application that uses biological systems, living organisms, or derivatives thereof, to make or modify products or processes for specific use." In fact, biotechnology is thousands of years old, used in agriculture, food manufacturing and medicine.

Industry-based, underground economy gross domestic product, by province and territory, current dollars.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_20bc96c5008315219f6820409e04ca38/view

Oracle’s cloud services and license support division is the company’s most profitable business segment, bringing in over ** billion U.S. dollars in its 2025 fiscal year. In that year, Oracle brought in annual revenue of close to ** billion U.S. dollars, its highest revenue figure to date. Oracle Corporation Oracle was founded by Larry Ellison in 1977 as a tech company primarily focused on relational databases. Today, Oracle ranks among the largest companies in the world in terms of market value and serves as the world’s most popular database management system provider. Oracle’s success is not only reflected in its booming sales figures, but also in its growing number of employees: between fiscal year 2008 and 2021, Oracle’s total employee number has grown substantially, increasing from around ****** to *******. Database market The global database market reached a size of ** billion U.S. dollars in 2020. Database Management Systems (DBMSs) provide a platform through which developers can organize, update, and control large databases, with products like Oracle, MySQL, and Microsoft SQL Server being the most widely used in the market.

Dataset Documentation Tools Market Outlook

According to our latest research, the global dataset documentation tools market size reached USD 1.14 billion in 2024, reflecting the increasing emphasis on data governance and transparency across industries. The market is projected to grow at a robust CAGR of 20.8% from 2025 to 2033, with a forecasted value of USD 7.49 billion by 2033. This substantial growth is primarily driven by the rising adoption of artificial intelligence and machine learning, which demand high-quality, well-documented datasets for optimal performance and compliance.

The primary growth factor for the dataset documentation tools market is the exponential increase in data generation across sectors such as healthcare, finance, retail, and government. Organizations are increasingly recognizing the critical importance of dataset documentation for ensuring data accuracy, traceability, and compliance with regulatory standards. The proliferation of big data analytics and AI-powered decision-making has further heightened the demand for robust documentation tools that facilitate seamless data discovery, lineage tracking, and metadata management. As businesses strive to unlock actionable insights from vast and complex datasets, comprehensive documentation tools have become indispensable for maintaining data integrity and supporting advanced analytics initiatives.

Another significant driver propelling the dataset documentation tools market is the evolving regulatory landscape surrounding data privacy and protection. Stringent regulations such as the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), and other region-specific frameworks are compelling organizations to adopt standardized documentation practices. These regulations mandate detailed record-keeping, transparency in data usage, and the ability to demonstrate compliance during audits. Dataset documentation tools not only streamline compliance efforts but also reduce the risk of data breaches, reputational damage, and legal penalties. As regulatory scrutiny intensifies globally, businesses are prioritizing investments in documentation solutions to mitigate risks and foster trust among stakeholders.

The rapid digital transformation across industries is also fueling the adoption of dataset documentation tools. Enterprises are embracing cloud computing, IoT, and digital platforms, resulting in increasingly complex and distributed data ecosystems. Managing and documenting these diverse data assets manually is no longer feasible, prompting organizations to deploy automated documentation solutions. These tools enhance collaboration among data teams, improve data accessibility, and accelerate the development of AI and analytics models. The integration of advanced features such as natural language processing, automated metadata extraction, and AI-driven data cataloging is further enhancing the value proposition of modern dataset documentation tools, enabling organizations to achieve greater efficiency and scalability in their data operations.

From a regional perspective, North America continues to dominate the dataset documentation tools market, accounting for the largest share in 2024. This leadership is attributed to the presence of major technology companies, early adoption of advanced data management solutions, and a mature regulatory environment. However, the Asia Pacific region is witnessing the fastest growth, driven by rapid digitalization, increasing investments in AI and analytics, and a burgeoning startup ecosystem. Europe also remains a significant market, supported by stringent data protection regulations and a strong focus on data quality and governance. As organizations worldwide recognize the strategic importance of data documentation, the market is expected to witness robust growth across all major regions, with emerging economies presenting lucrative opportunities for vendors.

Component Analysis

The dataset documentation tools market is segmented by component into software and services. The software segment holds the majority share, as organizations increasingly deploy advanced documentation platforms to automate and streamline their data management processes. These software solutions offer a comprehensive suite of features, including data cataloging, metadata management, lineage tracking, and collaborative documentation, catering to the diverse needs of enterprises across various industries. The i

Luxembourg LU: Foreign Direct Investment Income: Inward: USD: Total: Not Allocated data was reported at 0.000 USD mn in 2022. This stayed constant from the previous number of 0.000 USD mn for 2021. Luxembourg LU: Foreign Direct Investment Income: Inward: USD: Total: Not Allocated data is updated yearly, averaging 15.630 USD mn from Dec 2012 (Median) to 2022, with 10 observations. The data reached an all-time high of 11.493 USD bn in 2013 and a record low of 0.000 USD mn in 2022. Luxembourg LU: Foreign Direct Investment Income: Inward: USD: Total: Not Allocated data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Luxembourg – Table LU.OECD.FDI: Foreign Direct Investment Income: USD: by Industry: OECD Member: Annual. Reverse investment: Netting of reverse investment in equity (when a direct investment enterprise acquires less than 10% equity ownership in its parent) and reverse investment in debt (when a direct investment enterprise extends a loan to its parent) is applied in the recording of total inward and outward FDI transactions and positions. Treatment of debt FDI transactions and positions between fellow enterprises: directional basis according to the residency of the ultimate controlling parent (extended directional principle).; Under the directional presentation , the direct investment flows and positions are organised according to the direction of the investment for the reporting economy-either outward or inward . So, for a particular country, all flows and positions of direct investors resident in that economy are shown under outward investment and all flows and positions for direct investment enterprises resident in that economy are shown under inward investment. The directional presentation reflects the direction of influence. For more details, see a complete note on ' Asset/liability versus directional presentation '; FDI financial flows are cross-border transactions between affiliated parties (direct investors, direct investment enterprises and/or fellow enterprises) recorded during the reference period (typically year or quarter). FDI positions represent the value of the stock of direct investments held at the end of the reference period (typically year or quarter). The change in direct investment positions from one period to the next is equal to the value of financial transactions recorded during the period plus other changes in prices, exchange rates, and volume. FDI income data are closely linked to the stocks of investments and are used for analysis of the productivity of the investment and calculation of the rate of return on the total funds invested. The main financial instrument components of FDI are equity and debt instruments. Equity includes common and preferred shares (exclusive of non-participating preference shares which should be included under debt), reserves, capital contributions and reinvestment of earnings. Dividends, distributed branch earnings, reinvested earnings and undistributed branch earnings are components of FDI income on equity . Reinvested earnings and reinvestment of earnings are separately identified components of equity in FDI income data and in FDI financial flows. Debt instruments include marketable securities such as bonds, debentures, commercial paper, promissory notes, non-participating preference shares and other tradable non-equity securities as well as loans, deposits, trade credit and other accounts payable/ receivable.The interest returns on the above instruments are included in FDI income on debt .; FDI transactions and positions by partner country and/or by industry are available excluding and including resident Special Purpose Entities (SPEs). The dataset 'FDI statistics by parner country and by industry - Summary' contains series including resident SPEs only. Valuation method used for listed inward and outward equity positions: Market value. Valuation method used for unlisted inward and outward equity positions: Own funds at book value. Valuation method used for inward and outward debt positions: Market value, Nominal value.

The Gross Domestic Product (GDP) in Norway was worth 483.73 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Norway represents 0.46 percent of the world economy. This dataset provides the latest reported value for - Norway GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

According to Cognitive Market Research, the global GPU Database market size was USD 455 million in 2024 and will expand at a compound annual growth rate (CAGR) of 20.7% from 2024 to 2031. Market Dynamics of GPU Database Market Key Drivers for GPU Database Market Growing Demand for High-Performance Computing in Various Data-Intensive Industries- One of the main reasons the GPU Database market is growing demand for high-performance computing (HPC) across various data-intensive industries. These industries, including finance, healthcare, and telecommunications, require rapid data processing and real-time analytics, which GPU databases excel at providing. Unlike traditional CPU databases, GPU databases leverage the parallel processing power of GPUs to handle complex queries and large datasets more efficiently. This capability is crucial for applications such as machine learning, artificial intelligence, and big data analytics. The expansion of data and the increasing need for speed and scalability in processing are pushing enterprises to adopt GPU databases. Consequently, the market is poised for robust growth as organizations continue to seek solutions that offer enhanced performance, reduced latency, and greater computational power to meet their evolving data management needs. The increasing demand for gaining insights from large volumes of data generated across verticals to drive the GPU Database market's expansion in the years ahead. Key Restraints for GPU Database Market Lack of efficient training professionals poses a serious threat to the GPU Database industry. The market also faces significant difficulties related to insufficient security options. Introduction of the GPU Database Market The GPU database market is experiencing rapid growth due to the increasing demand for high-performance data processing and analytics. GPUs, or Graphics Processing Units, excel in parallel processing, making them ideal for handling large-scale, complex data sets with unprecedented speed and efficiency. This market is driven by the proliferation of big data, advancements in AI and machine learning, and the need for real-time analytics across industries such as finance, healthcare, and retail. Companies are increasingly adopting GPU-accelerated databases to enhance data visualization, predictive analytics, and computational workloads. Key players in this market include established tech giants and specialized startups, all contributing to a competitive landscape marked by innovation and strategic partnerships. As organizations continue to seek faster and more efficient ways to harness their data, the GPU database market is poised for substantial growth, reshaping the future of data management and analytics.< /p>

About this dataset

Context

The dataset presents the median household income across different racial categories in Industry town. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Industry town population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 93.44% of the total residents in Industry town. Notably, the median household income for White households is $63,125. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $63,125.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Industry town.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Industry town median household income by race. You can refer the same here